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| Author | : Ornella Cominetti |
| Publisher | : Frontiers Media SA |
| Total Pages | : 184 |
| Release | : 2023-05-12 |
| Genre | : Science |
| ISBN | : 2832523420 |
| Author | : Thorsten Joachims |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 218 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461509076 |
Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
| Author | : Indra Mani |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2024-06-10 |
| Genre | : Science |
| ISBN | : 9789819718436 |
This book introduces the rapidly evolving field of multi-omics in understanding the human microbiome. The book focuses on the technology used to generate multi-omics data, including advances in next-generation sequencing and other high-throughput methods. It also covers the application of artificial intelligence and machine learning algorithms to the analysis of multi-omics data, providing readers with an overview of the powerful computational tools that are driving innovation in this field. The chapter also explores the various bioinformatics databases and tools available for the analysis of multi-omics data. The book also delves into the application of multi-omics technology to the study of microbial diversity, including metagenomics, metatranscriptomics, and metaproteomics. The book also explores the use of these techniques to identify and characterize microbial communities in different environments, from the gut and oral microbiome to the skin microbiome and beyond. Towards theend, it focuses on the use of multi-omics in the study of microbial consortia, including mycology and the viral microbiome. The book also explores the potential of multi-omics to identify genes of biotechnological importance, providing readers with an understanding of the role that this technology could play in advancing biotech research. Finally, the book concludes with a discussion of the clinical applications of multi-omics technology, including its potential to identify disease biomarkers and develop personalized medicine approaches. Overall, this book provides readers with a comprehensive overview of this exciting field, highlighting the potential for multi-omics to transform our understanding of the microbial world.
| Author | : Abedalrhman Alkhateeb |
| Publisher | : Springer Nature |
| Total Pages | : 171 |
| Release | : 2023-12-15 |
| Genre | : Science |
| ISBN | : 303136502X |
The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
| Author | : Kim-Anh Lê Cao |
| Publisher | : CRC Press |
| Total Pages | : 316 |
| Release | : 2021-11-08 |
| Genre | : Computers |
| ISBN | : 1000472191 |
Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. There is a real need for good training in statistics, from data exploration through to analysis and interpretation. This book provides an overview of statistical and dimension reduction methods for high-throughput biological data, with a specific focus on data integration. It starts with some biological background, key concepts underlying the multivariate methods, and then covers an array of methods implemented using the mixOmics package in R. Features: Provides a broad and accessible overview of methods for multi-omics data integration Covers a wide range of multivariate methods, each designed to answer specific biological questions Includes comprehensive visualisation techniques to aid in data interpretation Includes many worked examples and case studies using real data Includes reproducible R code for each multivariate method, using the mixOmics package The book is suitable for researchers from a wide range of scientific disciplines wishing to apply these methods to obtain new and deeper insights into biological mechanisms and biomedical problems. The suite of tools introduced in this book will enable students and scientists to work at the interface between, and provide critical collaborative expertise to, biologists, bioinformaticians, statisticians and clinicians.
| Author | : |
| Publisher | : Elsevier |
| Total Pages | : 730 |
| Release | : 2018-09-22 |
| Genre | : Science |
| ISBN | : 0444640452 |
Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environmental, clinical and food analysis cases help readers disseminate these approaches. Chapters of note include an Introduction to Data Analysis Relevance in the Omics Era, Omics Experimental Design and Data Acquisition, Microarrays Data, Analysis of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS Metabolomic, Data Normalization and Scaling, Metabolomics Data Preprocessing, and more. Presents the best reference book for omics data analysis Provides a review of the latest trends in transcriptomics and metabolomics data analysis tools Includes examples of applications in research fields, such as environmental, biomedical and food analysis
| Author | : Gary Hardiman |
| Publisher | : MDPI |
| Total Pages | : 202 |
| Release | : 2020-04-15 |
| Genre | : Science |
| ISBN | : 3039287443 |
A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.
| Author | : Momiao Xiong |
| Publisher | : CRC Press |
| Total Pages | : 668 |
| Release | : 2017-12-01 |
| Genre | : Mathematics |
| ISBN | : 1498725805 |
Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.
| Author | : Guangyan Zhou |
| Publisher | : |
| Total Pages | : |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
"With advances in high-throughput molecular profiling technologies, there is an increase in types and quantities of omics data available for researchers to gain insights for biomedical research. However, effective extraction of information and integration of these data remain challenging due to the complex nature of multi-omics data. This thesis aims to address the challenges of transcriptomics data analysis and multi-omics data integration by developing easy-to-use, web-based platforms to support advanced statistics and visual analytics for broad bench scientists without programming expertise. Firstly, I developed the version 3.0 of NetworkAnalyst, a web-based platform for comprehensive analysis and interpretation of transcriptomics data. It supports both thorough data processing and comparative analysis including nested comparisons and time series. It offers a rich set of visual analytics methods encompassing network, volcano, heatmap, chord diagram, Venn diagram and scatter plot visualization coupled with molecular interaction and enrichment analysis for functional interpretation of transcriptomics data. NetworkAnalyst also allows meta-analysis of multiple gene expression tables or gene lists using a combination of advanced statistical meta-analysis methods and integrative visual analytics. Secondly, I developed OmicsNet, a web-based visual analytics platform dedicated for network-based multi-omics integration and visual exploration. The tool distinguishes itself by enabling web-based 3D visualization of biological networks in various innovative graphical layouts. By leveraging known molecular interactions from public databases, OmicsNet is able to build multi-omics interaction network from user supplied lists of molecules encompassing genes, proteins, miRNA, transcription factors and metabolites to facilitate holistic data understanding. Lastly, I developed OmicsAnalyst, a web-based platform that implements data-driven multi-omics integration methods coupled with advanced visual analytics. The tool supports three distinct strategies for multi-omics integration including correlation-based, clustering-based and dimension reduction-based approaches, coupled with network, heatmap and 3D scatter plot visual analytics respectively. OmicsAnalyst was able to integrate proteomics and metabolomics datasets to reveal important expression patterns and key biomarker signatures from a recent multi-omics study on human pregnancy. Overall, this thesis shows how web-based visual analytics frameworks can be used to facilitate omics data analysis processes and expedite data exploration process for hypothesis generation and more targeted studies"--
| Author | : Institute of Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 354 |
| Release | : 2012-09-13 |
| Genre | : Science |
| ISBN | : 0309224187 |
Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
